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LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@ariankordi
ariankordi / FFLTestingThumbProvider.cpp
Created July 13, 2025 01:29
Mii thumbnailer for Windows PoC using the FFL-Testing Mii renderer server
/**
* \file FFLTestingThumbProvider.cpp
* \author Arian Kordi (https://github.com/ariankordi)
* \date 2025/07/12
*
* \brief Windows thumbnail provider for Mii data files using
* the FFL-Testing Mii renderer server: https://github.com/ariankordi/FFL-Testing
* \details Requires setting up and running on default port of 12346.
* Not ideal for real use. Should be considered a proof-of-concept/toy.
* https://github.com/ariankordi
@rohitg00
rohitg00 / llm-wiki.md
Last active April 28, 2026 01:22 — forked from karpathy/llm-wiki.md
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

@jakebathman
jakebathman / user-agents.txt
Last active April 28, 2026 01:19
A list of major user agent strings for different browsers and devices
Chrome/Windows: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36
Chrome/Windows: Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36
Chrome/Windows: Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36
Chrome/macOS: Mozilla/5.0 (Macintosh; Intel Mac OS X 11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36
Chrome/Linux: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36
Chrome/iPhone: Mozilla/5.0 (iPhone; CPU iPhone OS 14_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) CriOS/87.0.4280.77 Mobile/15E148 Safari/604.1
Chrome/iPhone (request desktop): Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_5) AppleWebKit/605.1.15 (KHTML, like Gecko) CriOS/87 Version/11.1.1 Safari/605.1.15
Chrome/iPad: Mozilla/5.0 (iPad; CPU OS 14_3 like Mac OS X) AppleWebKit/605.1.1
@waylandzhang
waylandzhang / cc.md
Created January 19, 2026 03:07
Claude Code 最佳实践配置方案

Claude Code 配置与工作流完全指南

一、核心概念

1. Skills(技能)与 Commands(命令)

  • 用途:预设工作流的快捷方式,可链式调用(如 /refactor-clean/tdd/e2e
  • 存储位置
    • Skills:~/.claude/skills/ — 更广泛的工作流定义
    • Commands:~/.claude/commands/ — 快速执行的提示词